A Fuzzy Expert System Model for the Determination of Coronavirus Disease Risk

Coronavirus disease, also known as COVID-19, is a novel disease that has defied the understanding of medical practitioners globally. It is very infectious and there is no known cure or vaccine. The disease which originated from Wuhan city, Hubei province, China is responsible for over 800,000 deaths globally and over 23million confirmed infections. Infected individuals are identified by the symptoms they exhibit. Early detection is required to contain the virus and prevent fatalities. However, there is grossly limited testing kits in many countries and under-reporting of confirmed cases, thereby increasing the likelihood and threat of rapid spread of the disease. This study proposes a fuzzy expert system diagnostic model to aid early determination of infection risk using major clinical characteristics and symptoms. Relevant research findings were used to determine the fuzzy membership functions to handle the imprecision evident in this domain, as some of the symptoms are pointers to other diseases. The modelwas simulated with MATLAB and sample data tested. Results show that the system will be a handy decision support tool for early evaluation of people’s COVID-19 health status.